12 June 2017
treatment using LAR data occurs when one
individual is accorded different, less preferential,
treatment when compared to a similarly situated
individual on account of the disparately treated
person’s membership in a protected class. Because
the treatment is deemed to relate to the protected
basis, intent is often implied, and regulators and
enforcement agencies will look for comparative
evidence in LAR data to prove a fair lending
violation. However, if an institution can show that,
in making the credit decision, the outcome was
actually based on a legitimate difference that
justified treating one applicant more favorably than
the other, the appropriate conclusion is that the
applicants were not similarly situated, such that no
unlawful disparate treatment occurred.

Another type of disparate treatment analysis
that utilizes LAR data occurs on a geographical
basis. Sometimes there exists a statistically
significant difference in LAR data between similarly
situated, or peer, mortgage lenders in a geographic
area. This statistical difference forms the basis for
allegations of a type of disparate treatment known
as “redlining.” In contrast to the type of disparate
treatment revealed by a comparison of treatment
between individuals, redlining may be alleged
where a high-minority-concentration geographic
area appears to have been disparately treated,
or redlined, without regard to the fact that some
individual borrowers residing within that geographic
area may be qualified for a mortgage loan.

With respect to LAR data and its adequacy
in a disparate treatment analysis, whether on an
individual basis or a geographic basis, a 2015
press release from the Federal Financial Institutions
Examination Council (FFIEC) observed that the
current LAR data does not provide adequate
information on its own to assess a mortgage
lender’s compliance with fair lending law because
many “potential determinants of loan application
and pricing decisions” are absent from the current
LAR data set. These perceived inadequacies now
appear to be eliminated with the recently adopted
amendments to Regulation C, referred to by the
Consumer Financial Protection Bureau (CFPB) as the
“Final Rule.” However, as discussed below, the Final
Rule brings with it a potential set of new challenges.

The Final Rule, which is generally effective
January 1, 2018, adds 25 new data elements to the
existing LAR data set, and modifies and expands
many other existing elements. Certain of the new
elements, such as the combined loan-to-value ratio
(CLTV), credit score, debt-to-income ratio (DTI),
and automated underwriting system (AUS) results,
will provide regulators and enforcement agencies
information about lending practices that is currently
only available in a loan file-by-loan file review such
as occurs during a regulatory examination or under a
formal document request by an enforcement agency.
The positive aspects of this additional insight stand in
tension with new challenges, particularly with respect
to the ability of mortgage lenders to tell their own
story about the meaning of their data.

The lack of certain loan-level information in the
current LAR data set has resulted in regulatory and
enforcement agencies’ allegations of disparate
treatment redlining based primarily on a statistical
analysis of a mortgage lender’s application or
origination rate in comparison with that of other
mortgage lenders that are deemed to be its peers.
The increased visibility to loan-level information
provided by the Final Rule, as opposed to mere
statistical comparisons in the formulation of
allegations of fair lending violations, should allow
every mortgage lender to be judged on its own
merits, rather than on comparisons to the mortgage
lender’s deemed peers.

Further, the results of a fair lending analysis
based on actual data should be more predictable
and allow for better business planning. The
expanded LAR data set should allow a fair lending
inquiry to be more detailed, comprehensive and
focused on the activity of a mortgage lender and its
origination channel. This should facilitate a better
understanding of disparities in both underwriting
and pricing outcomes. New data elements such as
credit score, DTI, CLTV, interest rate, interest rate
spread, discount points, origination fees, and lender
credits will now be analyzed, not just in isolation,
but also in totality, to form a more clear overall
picture. Similarly, denial ratios based on AUS results
and action taken will replace the old matched-pair
review previously performed by agencies only under
an examination or investigation scenario. ∆